Technical Field
The present invention generally relates to methods and systems for remote sensing for object recognition. More particularly, the present disclosure is related to remote sensing using optical orbital angular momentum (OAM)-based spectroscopy for object recognition.
Description of Related Art
Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. In some instances, remote sensing enables collection of data in areas that may be dangerous or inaccessible. Accordingly, remote sensing is vital to an array of important scientific, environmental and social safety and security applications.
However, typical remote sensing methods fail to obtain high resolution spatial feature information about the object. For example, Light Detection and Ranging (LIDAR) is a remote sensing method in which light intensity is used to identify distance-based information to map the topography of an object/surface, its resolution is limited to the spot size of the light beam, which grows with increased distance. Consequently, remote sensing of high resolution features, such as small objects and sharp edges, is not possible using previous light-based remote sensing methods.
Remote sensing methods using non-OAM properties of the light beam (e.g., intensity, polarization, and wavelength) fail to obtain fine-resolution spatial information about the object, as discussed above. Orbital angular momentum of light (OAM) is the component of angular momentum of a light beam, such as the amount of rotation present in the light beam, that is dependent on the field spatial distribution, and not on the polarization (e.g., property of the wave which may oscillate in more than one orientation). Taking high resolution pixel-by-pixel images and/or videos of objects through, for example, low orbiting satellites, has also been proposed. However, in addition to requiring the capital expenditure (CAPEX) intensive acquisition and deployment of satellites, high resolution pixel-by-pixel images require large storage sizes and high bandwidth for image transmission, and can suffer from poor image quality and may require extensive and slow image post-processing.
The ability to overcome these limitations and perform high resolution object identification using LIDAR-based methods would, thus, be attractive to a range of important remote sensing applications.